Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning

Moon, Daeyoon; Chung, Siu-Wah; Kwon, Soon-Wook; Seo, Jongwon; Shin, Joonghwan

doi:10.1016/j.autcon.2018.07.020

Cited by 116 publications

(38 citation statements)

References 30 publications

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“…In particular, UAV photogrammetry has been used to acquire 3D numerical data across a wide area, which, when combined with terrestrial laser scanning data, provides a large number of point clouds; this reduces the time required for data acquisition. In this respect, studies on the digital documentation of cultural heritage sites using different technologies have been conducted in conjunction with the deployment of UAV [43][44][45][46][47]. In this paper, we focused on evaluating the reliability of the point data obtained via terrestrial laser scanning and UAV photogrammetry to enhance the convergent usability of both datasets by comparing the position accuracy of the point cloud data.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

Hong

2019

IJGI

117

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Three-dimensional digital technology is important in the maintenance and monitoring of cultural heritage sites. This study focuses on using a combination of terrestrial laser scanning and unmanned aerial vehicle (UAV) photogrammetry to establish a three-dimensional model and the associated digital documentation of the Magoksa Temple, Republic of Korea. Herein, terrestrial laser scanning and UAV photogrammetry was used to acquire the perpendicular geometry of the buildings and sites, where UAV photogrammetry yielded higher planar data acquisition rate in upper zones, such as the roof of a building, than terrestrial laser scanning. On comparing the two technologies’ accuracy based on their ground control points, laser scanning was observed to provide higher positional accuracy than photogrammetry. The overall discrepancy between the two technologies was found to be sufficient for the generation of convergent data. Thus, the terrestrial laser scanning and UAV photogrammetry data were aligned and merged post conversion into compatible extensions. A three-dimensional (3D) model, with planar and perpendicular geometries, based on the hybrid data-point cloud was developed. This study demonstrates the potential for using the integration of terrestrial laser scanning and UAV photogrammetry in 3D digital documentation and spatial analysis of cultural heritage sites.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

Hong

2019

IJGI

117

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“…Furthermore, a stereo system has limitations for real-life applications due to its considerable computational expense, which hinders its wide use in real-time measurements on conveyor belts. For rangebased methods, that is, ground LiDAR (also known as terrestrial laser scanning) and structure light technology, the surface of the object was scanned by laser or a stripe of light directly, and then the distance was determined based on the known relative positions of the light source and optical sensor [22,23]. Because of this feature, the two methods can provide accurate and complete details with a high degree of automation, and accurate geometric information of the object can be obtained.…”

Section: A 3d Point Cloud Acquisition Methodsmentioning

confidence: 99%

Evaluating Angularity of Coarse Aggregates Using the Virtual Cutting Method Based on 3D Point Cloud Images

et al. 2020

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In this paper, a new method called the Virtual Cutting Method is proposed to evaluate the angularity index (AI) values of 3D point cloud coarse aggregate images with the aim of characterizing the angularity of aggregates on conveyor belts. The 3D point cloud images of coarse aggregates were first captured, preprocessed, and segmented into single 3D aggregate objects. Based on the processed 3D aggregate images, intersection contours were extracted using a series of intersection planes with an equivalent angle between two adjacent planes. The AI was evaluated by averaging the angularity of the contours using the gradient method, which was used in the AIMS2 system. Statistical analysis was then performed to select the optimum angle between two adjacent planes. It was found that an angle of five degrees was the ideal angle, as it can balance the execution time and effectiveness of the method. Finally, the AI results of the Virtual Cutting Method were compared with those of 2D and 3D Projection Methods. It was found that the AI rankings of the three methods for different aggregate textures are generally consistent. The findings of this study conclude that the Virtual Cutting Method can be employed to quantify the angularity of a single aggregate or aggregates in piles on conveyor belts based on 3D point cloud images. INDEX TERMS Aggregate particles; Angularity evaluation; 3D point cloud image; Virtual Cutting

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“…Point cloud processing has been applied in a broad range of applications such as autonomous driving [15], robotics [16], CAD (Computer Aided Design) and sharp edge extraction [17], [18], photogrametry [19], and remote sensing [20].…”

Section: Related Workmentioning

confidence: 99%

DCG-Net: Dynamic Capsule Graph Convolutional Network for Point Clouds

Bazazian

Nahata

2020

IEEE Access

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This paper introduces DCG-Net (Dynamic Capsule Graph Network) to analyze point clouds for the tasks of classification and segmentation. DCG-Net aggregates point cloud features to build and update the graphs based on the dynamic routing mechanism of capsule networks at each layer of a convolutional network. The first layer of DGC-Net exploits the geometrical attributes of the point cloud to build a graph by neighborhood aggregation while the deeper layers of the network dynamically update the graph based on the feature space of convolutions. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet-Part. Our experimental results demonstrate that DCG-Net achieves state-of-theart performance on public datasets, 93.4% accuracy on ModelNet40, and 85.4% instance mIoU (mean Intersection over Union) on ShapeNet-Part.

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Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning

Cited by 116 publications

References 30 publications

Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

Evaluating Angularity of Coarse Aggregates Using the Virtual Cutting Method Based on 3D Point Cloud Images

DCG-Net: Dynamic Capsule Graph Convolutional Network for Point Clouds

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